Now, AI Can Give You an Edge… in Negotiations

AI is capable of performing many tasks that enhance human labor and thinking. Can it provide an advantage in the negotiation process as well?

Noted entrepreneur Victor Kiam once advised that “a negotiator should observe everything. You must be part Sherlock Holmes, part Sigmund Freud.” What if a machine can help provide those observational powers? Artificial intelligence (AI) is now capable of observing interactions, in real time, and delivered informed recommendations on how to proceed.

That’s the word from Matt Dixon, chief product and research officer at Tethr, who explained the increasing potential of AI for achieving a competitive edge in negotiating in a recent podcast at Negotiations Ninja.

Typically, negotiation has been known to be more art than science — the art of getting what you want or need from person-to-person interaction or bargaining. Nowadays, as more interaction occurs electronically — via email, phone, text chat or social media — there is quite a bit of science being applied to the negotiation process as well. Artificial intelligence (AI) is capable of performing many tasks that enhance human labor and thinking — so it only stands to reason that it can provide an advantage in the negotiation process as well.

There are several levels on which this is happening. As salespeople or customer service reps interact with customers electronically, they may be able to take advantage of real-time AI recommendations to help guide the engagement or transaction. “AI can help us to understand the flow of conversation,” Dixon says. “So as a salesperson or service professional is engaged with a customer in a conversation, in real time, the AI will be able to understand, perhaps even through the inflection in the customer’s voice, and make suggestions. This helps salespeople to counter the customer with a proposal or discount.”

Dixon adds that rather than monitor singular conversations and interject recommendations on a case-by-case basis, the AI-based negotiation system “is digesting lots of conversational data across lots of sales reps to try to understand where there are coaching opportunities, new training opportunities, value prop improvement opportunities, and product improvement opportunities.”

Then, there are chatbots, which can be trained to bargain with customers. “Chatbots are really, really good at negotiating,” says Dixon. “In fact, we found that service reps were giving twice the amount of discount to robots than they were going to actual people. So what is being done effectively is train these chatbots on negotiation techniques, where they became really really good negotiators, and we’re winning really great discounts for the customers who hired them.”

Next, there’s the application of AI against transaction or customer relationship management (CRM) data, to sift through responses and engagements to determine where and how companies may be missing opportunities. “You can apply operational, financial, or CRM data to understand patterns,” Dixon explains. “That might yield some insight around how we’re doing when it comes to negotiation. Leveraging AI — which is really just a way to understand data at scale in a way that humans can’t — to find patterns in the data that might take a person a really long time to find.”

For example, an AI algorithm “would tell you things like the discount rate that different reps have given away, or what, pricing they’ve been able to negotiate, or terms and conditions negotiated, and how that varies across the salesforce,” Dixon says. “That way you have some insights if you give away the farm or make too many concessions to certain customers in certain parts of the business.”

About Joe McKendrick

Joe McKendrick is RTInsights' Industry Insights Editor in charge of contributed case studies. He is a regular contributor to Forbes on digital, cloud and Big Data topics. He served on the organizing committee for the recent IEEE International Conference on Edge Computing (full bio). Follow him on Twitter @joemckendrick.